Neural networks for option pricing and hedging: a literature review

J Ruf, W Wang - arXiv preprint arXiv:1911.05620, 2019 - arxiv.org
Neural networks have been used as a nonparametric method for option pricing and hedging
since the early 1990s. Far over a hundred papers have been published on this topic. This …

Reinforcement learning in economics and finance

A Charpentier, R Elie, C Remlinger - Computational Economics, 2021 - Springer
Reinforcement learning algorithms describe how an agent can learn an optimal action policy
in a sequential decision process, through repeated experience. In a given environment, the …

Neural networks-based algorithms for stochastic control and PDEs in finance

M Germain, H Pham, X Warin - arXiv preprint arXiv:2101.08068, 2021 - cambridge.org
This chapter presents machine learning techniques and deep reinforcement learning-based
algorithms for the efficient resolution of nonlinear partial differential equations and dynamic …

Trends and applications of machine learning in quantitative finance

S Emerson, R Kennedy, L O'Shea… - … conference on economics …, 2019 - papers.ssrn.com
Recent advances in machine learning are finding commercial applications across many
industries, not least the finance industry. This paper focuses on applications in one of the …

A quantum algorithm for linear PDEs arising in finance

F Fontanela, A Jacquier, M Oumgari - SIAM Journal on Financial Mathematics, 2021 - SIAM
We propose a hybrid quantum-classical algorithm, which originated from quantum
chemistry, to price European and Asian options in the Black--Scholes model. Our approach …

Assessing US insurance firms' climate change impact and response

A Gupta, A Owusu, J Wang - The Geneva Papers on Risk and Insurance …, 2024 - Springer
Climate change poses a serious risk for insurance firms, threatening their sustainability from
numerous channels of impact. Assessing this impact, however, is not straightforward. We …

Accelerated American option pricing with deep neural networks

D Anderson, U Ulrych - Quantitative Finance and Economics, 2023 - papers.ssrn.com
Given the competitiveness of a market-making environment, the ability to speedily quote
option prices consistent with an ever-changing market environment is essential. Thus, the …

Optimal insurance strategies: A hybrid deep learning Markov chain approximation approach

X Cheng, Z Jin, H Yang - ASTIN Bulletin: The Journal of the IAA, 2020 - cambridge.org
This paper studies deep learning approaches to find optimal reinsurance and dividend
strategies for insurance companies. Due to the randomness of the financial ruin time to …

Insurance valuation: A two-step generalised regression approach

K Barigou, V Bignozzi, A Tsanakas - … Bulletin: The Journal of the IAA, 2022 - cambridge.org
Current approaches to fair valuation in insurance often follow a two-step approach,
combining quadratic hedging with application of a risk measure on the residual liability, to …

Sample average approximation of CVaR-based hedging problem with a deep-learning solution

C Peng, S Li, Y Zhao, Y Bao - The North American Journal of Economics …, 2021 - Elsevier
Abstract Conditional Value-at-Risk (CVaR) is an extremely popular risk measure in finance
and is usually optimized to reduce the risk of large losses. This paper considers the CVaR …